Browsing by Subject "mobile robots"
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Item Robotic Searching for Stationary, Unknown and Transient Radio Sources(2012-07-16) Kim, Chang YoungSearching for objects in physical space is one of the most important tasks for humans. Mobile sensor networks can be great tools for the task. Transient targets refer to a class of objects which are not identifiable unless momentary sensing and signaling conditions are satisfied. The transient property is often introduced by target attributes, privacy concerns, environment constraints, and sensing limitations. Transient target localization problems are challenging because the transient property is often coupled with factors such as sensing range limits, various coverage functions, constrained mobility, signal correspondence, limited number of searchers, and a vast searching region. To tackle these challenge tasks, we gradually increase complexity of the transient target localization problem such as Single Robot Single Target (SRST), Multiple Robots Single Target (MRST), Single Robot Multiple Targets (SRMT) and Multiple Robots Multiple Targets (MRMT). We propose the expected searching time (EST) as a primary metric to assess the searching ability of a single robot and the spatiotemporal probability occupancy grid (SPOG) method that captures transient characteristics of multiple targets and tracks the spatiotemporal posterior probability distribution of the target transmissions. Besides, we introduce a team of multiple robots and develop a sensor fusion model using the signal strength ratio from the paired robots in centralized and decentralized manners. We have implemented and validated the algorithms under a hardware-driven simulation and physical experiments.Item Vision-based Navigation for Mobile Robots on Ill-structured Roads(2010-01-16) Lee, Hyun NamAutonomous robots can replace humans to explore hostile areas, such as Mars and other inhospitable regions. A fundamental task for the autonomous robot is navigation. Due to the inherent difficulties in understanding natural objects and changing environments, navigation for unstructured environments, such as natural environments, has largely unsolved problems. However, navigation for ill-structured environments [1], where roads do not disappear completely, increases the understanding of these difficulties. We develop algorithms for robot navigation on ill-structured roads with monocular vision based on two elements: the appearance information and the geometric information. The fundamental problem of the appearance information-based navigation is road presentation. We propose a new type of road description, a vision vector space (V2-Space), which is a set of local collision-free directions in image space. We report how the V2-Space is constructed and how the V2-Space can be used to incorporate vehicle kinematic, dynamic, and time-delay constraints in motion planning. Failures occur due to the limitations of the appearance information-based navigation, such as a lack of geometric information. We expand the research to include consideration of geometric information. We present the vision-based navigation system using the geometric information. To compute depth with monocular vision, we use images obtained from different camera perspectives during robot navigation. For any given image pair, the depth error in regions close to the camera baseline can be excessively large. This degenerated region is named untrusted area, which could lead to collisions. We analyze how the untrusted areas are distributed on the road plane and predict them accordingly before the robot makes its move. We propose an algorithm to assist the robot in avoiding the untrusted area by selecting optimal locations to take frames while navigating. Experiments show that the algorithm can significantly reduce the depth error and hence reduce the risk of collisions. Although this approach is developed for monocular vision, it can be applied to multiple cameras to control the depth error. The concept of an untrusted area can be applied to 3D reconstruction with a two-view approach.